Pen-controlled computing devices, such as Personal Digital Assistants (PDAs) and tablet computers, are finding increased commercial relevance. Such devices typically replace or supplement a traditional mouse and keyboard with a pen that serves both as a pointing device and as a device for entering “digital ink”. In many applications, the digital ink can represent both text and non-text data. For example, a user may use the pen to enter text, to draw sketches, and to indicate editing commands (e.g., deleting a text word by simply crossing out the word with the pen).
Some features extracted from an individual pen stroke provide some relevant information regarding the classification of the stroke as text or non-text (e.g., a full page circle may be considered a graphic circle, instead of a text ‘O’), so some limited separation between text and non-text data may be obtained. However, existing approaches tend to attempt such limited classification when the stroke is initially entered and do not adapt their initial classification as additional data context is received from the pen. Therefore, more subtle distinctions between text and non-text are not available in existing approaches. Accordingly, the accuracy and extent of existing approaches in distinguishing between the different data input modes of a pen (e.g., text and non-text) is inadequate.